A Bayesian-Based Approach to Human Operator Intent Recognition in Remote Mobile Robot Navigation
This work addresses the challenge of improving human-robot interaction for remote mobile robot navigation, though it appears incremental as it builds on existing probabilistic methods.
The paper tackles the problem of recognizing human operator intent in teleoperated robot navigation by proposing a Bayesian-based method, which outperforms existing methods in accuracy and uncertainty reduction in experimental evaluations.
This paper addresses the problem of human operator intent recognition during teleoperated robot navigation. In this context, recognition of the operator's intended navigational goal, could enable an artificial intelligence (AI) agent to assist the operator in an advanced human-robot interaction framework. We propose a Bayesian Operator Intent Recognition (BOIR) probabilistic method that utilizes: (i) an observation model that fuses information as a weighting combination of multiple observation sources providing geometric information; (ii) a transition model that indicates the evolution of the state; and (iii) an action model, the Active Intent Recognition Model (AIRM), that enables the operator to communicate their explicit intent asynchronously. The proposed method is evaluated in an experiment where operators controlling a remote mobile robot are tasked with navigation and exploration under various scenarios with different map and obstacle layouts. Results demonstrate that BOIR outperforms two related methods from literature in terms of accuracy and uncertainty of the intent recognition.